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Soil microbial community successional patterns during forest ecosystem restoration. 2
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Natasha C. Banninga*
, Deirdre B. Gleesona, Andrew H. Grigg
b, Carl D. Grant
b, Gary L. 4
Andersenc, Eoin L. Brodie
c and D.V. Murphy
a 5
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aSoil Biology Group, School of Earth and Environment, The University of Western Australia, 35 7
Stirling Highway, Crawley, WA 6009, Australia 8
bAlcoa of Australia, Huntly Mine, PO Box 172, Pinjarra, WA 6208, Australia 9
cEcology Department, Earth Sciences Division, Lawrence Berkeley National Laboratory, 10
Berkeley, CA 94720, USA 11
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Running title: Microbial succession during forest restoration 13
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Copyright © 2011, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved.Appl. Environ. Microbiol. doi:10.1128/AEM.00764-11 AEM Accepts, published online ahead of print on 1 July 2011
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Abstract 15
Soil microbial community characterisation is increasingly being used to determine the response 16
of soils to stress and disturbance and to assess ecosystem sustainability. However, there is little 17
experimental evidence to indicate that predictable patterns in microbial community structure or 18
composition occur during secondary succession or ecosystem restoration. This study utilised a 19
chronosequence of developing jarrah (Eucalyptus marginata) forest ecosystems, rehabilitated 20
after bauxite mining (up to 18 years old), to examine changes in soil bacterial and fungal 21
community structures (by automated ribosomal intergenic spacer analysis; ARISA) and changes 22
in specific soil bacterial phyla by 16S rRNA gene microarray analysis. This study demonstrated 23
that mining in these ecosystems significantly altered soil bacterial and fungal community 24
structures. The hypothesis that the soil microbial community structures would become more 25
similar to surrounding non-mined forest with rehabilitation age was broadly supported by shifts 26
in the bacterial but not the fungal community. Microarray analysis enabled the identification of 27
clear successional trends in the bacterial community at the phylum-level and supported the 28
finding of an increase in similarity to non-mined forest soil with rehabilitation age. Changes in 29
soil microbial community structure were significantly related to the size of the microbial biomass 30
as well as numerous edaphic variables (including pH and C, N and P nutrient concentrations). 31
These findings suggest that soil bacterial community dynamics follow a pattern in developing 32
ecosystems that may be predictable and can be conceptualised as providing an integrated 33
assessment of numerous edaphic variables. 34
Keywords: ARISA/forest soil/ /microbial succession/phylogenetic microarray/rehabilitation 35
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Introduction 36
Soil microbial community structure and composition measures are increasingly being used to 37
assess ecosystem responses to anthropogenic disturbance and provide an indicator of ecosystem 38
recovery (30, 41, 60). However, in comparison to plant communities, there is limited 39
experimental evidence that predictable patterns in microbial community structure or composition 40
occur during secondary succession (18, 37) or ecosystem restoration (27, 33). Microbial 41
communities are able to respond more rapidly than plant communities to changes in 42
environmental conditions and may provide an early indication of the recovery trajectory (29). 43
However, the high level of sensitivity to numerous environmental factors can also result in long-44
term shifts (in the order of decades or more) in microbial community structure in rehabilitated 45
ecosystems (33). Following extreme disturbance, such as mining, even best-practice 46
rehabilitation programs may be expected to leave a soil legacy in terms of some alteration to the 47
soil organo-physico-chemical environment. 48
Edaphic factors that are purported to be significant drivers of soil microbial community 49
structure include soil pH (20, 52, 61), the quantity, quality and availability of soil carbon (C; 5, 50
11, 47) and nitrogen (N; 48, 53), soil water (17, 28), texture (12) and mineralogy (23). These 51
factors may exert an influence on microbial community structure simultaneously and produce 52
interactive and feedback effects (1). Thus, microbial community structure measures could be 53
conceptualised as an integrated assessment of numerous soil and ecosystem characteristics. 54
However, comprehensive characterisation of soil microbial community dynamics during 55
ecosystem restoration has been limited by the enormous microbial diversity within soils (59, 64). 56
In south-western Australia, bauxite mining within the Jarrah (Eucalyptus marginata) 57
forest has created a mosaic landscape of rehabilitation forest in various states of succession 58
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alongside non-mined forest. This industrial-scale mining and rehabilitation program (covering an 59
area of 13,000 ha to date) with a documented management history over 40 years (36) enables 60
utilisation of a chronosequence (space-for-time substitution) design. The existence of gradients 61
in soil chemical (pH, total C, N) and biological characteristics (microbial biomass and activity) 62
in these rehabilitation sites has been established previously (4). The two central hypotheses 63
tested in this study were i) that the mining disturbance would change soil microbial community 64
structure and ii) that the community structure would recover over time and become more similar 65
to non-mined forest soils with rehabilitation age. Considering the sensitivity of soil microbial 66
community structure to edaphic variables; we also sought to elucidate which soil characteristics 67
might be most important in driving microbial successional change. 68
Materials and methods 69
Study area 70
The study sites were located in the northern jarrah (Eucalyptus marginata) forest region of 71
Western Australia, approximately 110 km SSE of Perth (32°38’S, 116°06’E). The jarrah forest is 72
a dry sclerophyll type growing in a Mediterranean-type climate in highly weathered, lateritic, 73
sandy soils with low concentrations of major nutrients such as N and P (42). Bauxite mining has 74
been conducted in the area since 1963 and currently Alcoa of Australia clears, mines and 75
rehabilitates around 550 ha of forest per year (36). Detailed descriptions of Alcoa’s mining and 76
rehabilitation practices have been published elsewhere (25, 36). Briefly, mining involves the 77
complete removal of vegetation and surface soils (average removal up to 40 cm depth) in order 78
to access the bauxite ore. Rehabilitation involves re-landscaping of the mined site, return of 79
topsoil, surface contour ripping, seeding with native overstorey species (with the exception of 80
pre-1988 sites in which some non-native tree species were used) and understorey species, 81
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including N2-fixing legumes, and fertilisation with di-ammonium phosphate (DAP). The 82
concentration of legumes in the understorey seed mix and fertiliser application rates have 83
changed over time and both have been decreased in more recent rehabilitation. In this study, the 84
vegetated rehabilitation sites had been seeded with a legume density between 0.6-1.0 kg ha-1
and 85
fertilised with 500 kg ha-1
DAP. Ripping produces a distinct surface micro-topography (termed 86
mounds and furrows) known to impact on litter accumulation (58) and topsoil characteristics 87
(39). 88
Soil sampling and soil characteristics 89
Thirty sites were selected encompassing five replicates of four rehabilitation ages post-mining 90
(0-, 6-, 14- and 18-year-old) and five replicates of two site-vegetation types of jarrah forest 91
prevalent in the region and commonly targeted for mining. The site-vegetation forest types (S 92
and TS), classified according to the Havel system (31), are all dominated by a jarrah and marri 93
(Corymbia calophylla) overstorey but with differences in understorey species composition. 94
These floristic differences are related to topography and soil characteristics, with T-type sites 95
commonly found on loamier soils in higher rainfall areas than S-type sites. The management 96
history and vegetation characteristics of the sampled sites have been described previously (4) and 97
a map of the sampling locations is provided (Figure S1). Composite soil samples were collected 98
from three plots per site positioned to encompass topographical variation. Within rehabilitation, 99
soil from the highest and lowest point of the mounds and furrows, respectively, was collected 100
separately, to a depth of 5 cm as previous studies have shown that soil nutrients in jarrah forest 101
are concentrated in the top 5 cm (26). Soil cores were collected at random points within each plot 102
to a weight of 3 kg and combined (i.e. composite sample of 9 kg). Soil was sieved (< 4 mm) and 103
stored at 4°C before biochemical characterisations or frozen at -40°C for DNA extraction. 104
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The soil texture, water holding capacity, EC, pH (CaCl2), total C and N, bicarbonate-105
extractable (available) P, inorganic N, soluble organic C and N and microbial biomass C 106
concentrations have been described previously (4). This study utilised a sub-set of the previously 107
described experimental design and the relevant soil characteristics are given in supplementary 108
material (Table S1). 109
Automated Ribosomal Intergenic Spacer Analysis (ARISA) 110
Total soil DNA was extracted from 0.8 g of each composite 0-5 cm soil sample using the 111
UltraCleanTM
soil DNA isolation kit (Mo Bio Laboratories, Inc., USA). The manufacturer’s 112
instructions were modified to perform cell lysis using a Mini Bead Beater (BioSpec Products, 113
Inc) at 2500 rpm for 2 mins. The bacterial intergenic spacer region between the 16S and 23S 114
rRNA genes was amplified using FAM-labelled forward primer S-D-Bact-1522-b-S-20 and 115
reverse primer L-D-Bact-132-a-A-18 (45). The two fungal intergenic spacer regions spanning the 116
5.8S rRNA gene were amplified using the fungal-specific FAM-labelled forward primer ITS1-F 117
(21) and universal reverse primer ITS4 (62). PCR cycling conditions have been described 118
previously by Gleeson et al. (22, 23). Triplicate PCR amplifications were pooled and cleaned 119
with Wizard®
PCR Preps DNA purification system (Promega Corporation, Australia). 120
Intergenic fragment lengths were determined using an ABI 3730 automated sequencer 121
with 20 bp to 1200 bp size standards, using GeneMapper v4.0 software (Applied Biosystems). 122
Fragments smaller than 200 bp and larger than 1200 bp were excluded from the profiles. Profiles 123
of ribotype abundances (based on peak heights) were created using the program RiboSort (55) 124
within the statistical package R version 2.6.0 (13). Fragment sizes that differ by less than 0.5 bp 125
were considered to be identical ribotypes. Only fragments with fluorescence greater than 1% of 126
the total fluorescence summed across all samples were included. The majority of B-ARISA 127
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fragments were 300-820 bp in length, which is typical of soil bacterial ARISA profiles (51). The 128
majority of F-ARISA fragments were 500-800 bp in length. 129
PhyloChip microarray analyses 130
A sub-set of the DNA extracts, including furrow soils of each rehabilitation age and S-type non-131
mined forest soil, were used for microarray analyses. These were selected as representative of the 132
larger sample set, based on the ARISA profiles. Three out of five field replicates were randomly 133
selected for PCR amplification of bacterial 16S rRNA genes using primers 27F and 1492R (63). 134
Replicate PCR amplifications were performed for each sample (Eppendorf MasterCycler) using 135
eight different annealing temperatures between 48 and 58oC, to encompass a range of primer-136
template specificities, using cycling conditions described previously (16). Replicate PCR 137
products were pooled, cleaned and concentrated by ethanol precipitation. 138
For application onto the PhyloChip, 1000 ng of bacterial 16S rRNA gene amplicons were 139
fragmented, biotin labelled and hybridized as described earlier (6). The PhyloChip can resolve 140
8,434 bacterial taxa using an average of 24 perfect-match-mismatch probe pairs per taxon (7). 141
For a taxon to be reported as present in a sample, 90% of the probe-pairs in its set must have 142
been positive. The criteria used to score a probe-pair as positive has been described previously 143
(16). Hybridisation scores for each taxon, which are an average of the differences between 144
perfect match and mismatch fluorescent intensity of all probe pairs (excluding the highest and 145
lowest), were normalised using the fluorescence intensity of internal standards (6) and log 146
transformed to represent the relative abundance of each taxon. The relative abundance data for 147
each taxon within a phylum or class was summed to allow comparison between samples at 148
higher taxonomic ranks. 149
Statistical Analyses 150
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Overall differences in soil characteristics were tested by one-way analysis of variance using 151
GenStat v12 (VSN International Ltd, Hemel Hempstead, UK). All multivariate statistical 152
routines were conducted using PRIMER 6 & PERMANOVA+ (Primer-E Ltd., Plymouth, UK; 153
14). Multivariate analyses of ARISA and PhyloChip profiles were based on Bray–Curtis 154
dissimilarities on log transformed data, standardised by sample sum. Bray-Curtis was chosen as 155
it is unchanged by inclusion or exclusion of variables which are jointly absent between samples. 156
Analyses of ARISA profile data transformed to presence/absence were also performed. To 157
visualise differences between treatments, ordinations were performed by principal coordinate 158
analysis (PCO). Tests of the null hypothesis that there are no differences among a priori defined 159
groups were performed by permutational multivariate analysis of variance (PERMANOVA) (2). 160
Significance of the treatments “age”, “position” and their interaction were tested within 161
rehabilitation sites. Differences between non-mined sites and each rehabilitation age (with 162
mound and furrow separate) were tested by pairwise comparisons. 163
Relationships between changes in microbial community structure and individual soil 164
characteristics were analysed using distance-based multivariate multiple regression (DistLM). 165
Two-sided significance tests were used to determine whether a correlation was significantly 166
different from zero. Soil characteristics were then subjected to a forward selection procedure to 167
develop a model to explain the variance in profile data, taking into account the co-variance 168
between soil characteristics. Pearson correlations of individual soil variables with PCO axes 169
were also performed. 170
Results 171
Bacterial and fungal community structure by ARISA 172
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Rehabilitation age and micro-topographical position were found to be significant factors 173
affecting soil bacterial and fungal community structures (P < 0.05). All rehabilitation soils had 174
significantly different bacterial and fungal community structures to both non-mined forest 175
reference soils (P < 0.05) but the two non-mined site-vegetation types were not different from 176
each other (P > 0.1). The average Bray-Curtis similarity between rehabilitation and non-mined 177
forest soils was 21% for 0-year-old rehabilitation, increasing to 30% for 14-year-old 178
rehabilitation (and decreasing again to 27% for 18-year-old rehabilitation). All non-mined forest 179
soils had an average Bray-Curtis similarity of 40% to each other. This suggests a trend of 180
increasing similarity of bacterial community structures in 0- to 14-year-old rehabilitation to non-181
mined forest soils, which is also evident in the ordination (Figure 1a). This trend was also 182
evident with data transformed to a binary matrix i.e use of composition information only. 183
Rehabilitation age was also a significant factor affecting soil fungal community structure (Figure 184
1b). However, based on the comparison of Bray-Curtis similarities, there was no clear trend of 185
increasing similarity between rehabilitation and non-mined forest soils with age. The average 186
Bray-Curtis similarity in fungal community structure between all rehabilitation ages and non-187
mined forest soils was 16%, with non-mined forest soils having an average similarity to each 188
other of 30%. 189
Bacterial community analysis by microarray 190
A total of 2673 bacterial taxa were detected and the total richness within each phylum is given in 191
supplementary material (Table S2). All nine phyla that typically dominate 16S rRNA gene 192
libraries from soils (34) were represented. Other phylum-level lineages that have been found in 193
soil clone libraries elsewhere were also detected in the microarray analysis, such as Chlorobi, 194
Cyanobacteria, BRC1, Nitrospirae, OP10, Termite group I, TM6, TM7 (Table S2). The trend in 195
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bacterial community structure detected by the microarray analysis mirrored that detected by B-196
ARISA profiling, suggesting an increase in similarity to non-mined forest soils with 197
rehabilitation age (Figure 1c). PERMANOVA tests, using all taxa as individual variables, 198
suggested that 0- and 6-year-old rehabilitation soils were significantly different to the non-mined 199
forest soil (P < 0.05), but that 14- and 18-year old rehabilitation soils were not. 200
The changes in relative abundance of the major soil bacterial phyla followed three 201
distinct trends with respect to rehabilitation age: i) decreasing (Bacteroidetes, Firmicutes), ii) 202
increasing (Chloroflexi, Planctomycetes, Proteobacteria, Verrucomicrobia) or iii) no change or 203
no consistent trend (Acidobacteria, Actinobacteria, Gemmatimonadates); (Figure 2a-c). The first 204
two trends both contributed to an increase in similarity to the non-mined forest soil with 205
rehabilitation age. Proteobacteria is the most well represented phylum in culture collections, 206
rRNA gene databases and on the PhyloChip (with 1172 probe sets). Analysed at the class level, 207
the largest change with rehabilitation age occurred for the γ-Proteobacteria, which increased 208
during early rehabilitation, and β-Proteobacteria, which decreased with rehabilitation age 209
(Figure 2d). 210
Relationships between microbial community profiles and soil characteristics 211
The soil chemical and biological characteristics exhibited a gradient with rehabilitation 212
age with distinct heterogeneity between micro-topographical positions (Table S1). These soil 213
characteristics have been described previously (4) but can be summarised as exhibiting three 214
broad trends: i) increasing with rehabilitation age and becoming more similar to non-mined 215
forest (Ctot, Ntot, Csol org, Nsol org, Cmic, Cmic:Corg, WHC); ii) decreasing with age and becoming 216
less similar to non-mined forest (pH and C:Ntot) and iii) no differences between non-mined forest 217
and rehabilitation at any age (inorganic N, C:Nsol org). The exception was available P which was 218
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low in non-mined forest and 0-year-old rehabilitation (collected pre-fertilisation), highest in 6-219
year-old rehabilitation and then decreased with age. 220
Significant relationships were found between almost all soil characteristics tested 221
individually and the bacterial and fungal community structure profiles. However, there were 222
many significant correlations between the measured characteristics (supplementary Table S3). 223
Pearson correlations with the PCO axes (Figures 1a-c) demonstrated that bacterial and fungal 224
community structures changes were negatively correlated with the decline in soil pH and C:Ntot 225
ratio and positively correlated with the increase in soil organic matter (Ctot, Ntot) and microbial 226
biomass (Cmic). Only available P displayed a negative correlation with PCO axis 2, and 227
represents the separation in microbial community structures between fertilised rehabilitation soils 228
and unfertilised soils (0-year-old rehabilitation and non-mined forest). Forward selection models 229
identified between six and nine soil variables which explained up to 36% of the variance in the 230
ARISA profiles and 65% of variance in the microarray data (Table 1). Three soil variables; pH, 231
microbial biomass and total C, were significant explanatory variables in the forward selection 232
models of all three microbial community profiles. 233
Discussion 234
Bauxite mining in the jarrah forest of south-western Australia is known to result in microbial 235
biomass declines in topsoil (losses of more than 80% were estimated by comparison with non-236
mined forest soils) and alteration of several soil physico-chemical characteristics (4). The 237
hypothesis in this study that the mining-induced disturbance, which involves a number of soil 238
perturbations such as increased temperature, desiccation, physical disruption and loss of organic 239
matter, would also alter soil microbial community structure was supported. Successional change 240
in microbial community structure is likely to be driven by the availability of limiting resources 241
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and the ability of populations to utilise these resources (under altered physico-chemical 242
conditions), as hypothesised for plant community succession (57). There are many factors 243
influencing resource availability to the soil microbial populations in this rehabilitation 244
chronosequence. Nonetheless, we hypothesised that as the rehabilitation matures, the soil 245
microbial community structure would become more similar to the surrounding non-mined forest 246
soils. This hypothesis was supported by a trend of increasing similarity in bacterial community 247
structure to non-mined forest between 0 and 14 years of rehabilitation, up to 26% Bray-Curtis 248
similarity. Comparisons within non-mined forest soils only suggested that the maximum Bray-249
Curtis similarity achievable in the rehabilitation was around 37%. 250
The similarity in bacterial community structure between 14-year-old rehabilitation and 251
non-mined forest is higher than the 14% Bray-Curtis similarity reported between similar site 252
comparisons of vegetation structure (44). Similarities in vegetation structure within non-mined 253
forest sites averaged 34% but no age-related trend in rehabilitation vegetation structure toward 254
that of the non-mined forest was found. This supports the suggestion that soil microbial 255
community structure comparisons may provide an earlier indicator of the recovery trajectory 256
than vegetation structure comparisons (29). However, the time-frame for detecting recovery 257
trends in the soil bacterial community following extreme disturbance is still beyond a decade. 258
This time-frame is similar to that required for microbial biomass recovery in the rehabilitation 259
forest soils, and roughly comparable to indications elsewhere of 20 to 30 or more years for 260
bacterial community recovery after disturbance (27, 33, 43). 261
Significant relationships were found between most of the soil characteristics measured 262
and the microbial community profiles. Soil characteristics varied in response to rehabilitation age 263
(e.g. microbial and organic C, water holding capacity), changes in vegetation structure (e.g. 264
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declines in soil C:N ratio and pH as a consequence of high legume density in rehabilitation) and 265
fertilisation practices (e.g. high available P in 6- and 14-year old rehabilitation which also 266
favours N2-fixation by legumes and contributes to the observed declines in C:N ratio and pH). 267
The co-variation of many soil variables in developing ecosystems makes it difficult to assess the 268
significance of individual soil variables despite the use of forward selection models. Nonetheless, 269
the number and extent of correlations between microbial community structure and the soil 270
variables supports the conceptualisation of microbial community structure as an integrated 271
assessment of the edpahic environment. 272
Unlike the bacterial community, the fungal community structure did not exhibit a trend of 273
increasing similarity to non-mined forest soils, with overall lower levels of similarity. The fungal 274
primers used in this study have been reported to predominantly amplify basiodiomycetes and 275
ascomycetes (21, 35); and therefore are likely to include ectomycorrhizal fungi, known to be 276
associated with many jarrah forest plant species (8), and free-living saprophytes but not the 277
arbuscular mycorrhizal fungi. Previously, it has been reported that the richness of 278
ectomycorrhizal fungal species recovers during jarrah forest rehabilitation, but the species 279
composition remains different (24). Thus, analyses of fungal community structure in soil (this 280
study) and previously in root tips and sporocarps (24) have both indicated that differences 281
between rehabilitation and non-mined forest are likely to persist for more than 16-18 years. This 282
is the first study to show a relationship between soil fungal (and to a lesser extent bacterial) 283
community structure and differences in available P as a consequence of fertilisation in these 284
ecosystems. Elsewhere, increases in soil P availability have also been associated with declines of 285
root-associated fungal diversity (9) and changes in whole soil fungal community structure (40). 286
Longer-term shifts in the soil fungal community structure, compared to the bacterial, may also be 287
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more directly linked to the persistent differences in vegetation structure between rehabilitation 288
and non-mined forest (44), due to fungal mycorrhizal associations. There is also some evidence 289
that fungi, in particular mycorrhizal fungi, may have less efficient dispersal and colonising 290
abilities than bacteria, which may also contribute to a slower recovery of fungal communities 291
following disturbances that involve vegetation removal (32). 292
To determine whether there are predictable successional patterns in soil microbial 293
communities of relevance to a broader range of post-disturbance ecosystems, it is necessary to 294
identify changes in groups of phylogenetically or functionally-related populations. It has recently 295
been considered that bacteria at high taxonomic ranks, such as the phylum or class level, may 296
display ecological coherence (19, 49, 50). Ecological coherence implies that, despite the 297
physiological diversity between bacteria within a phylum, there may be some general life 298
strategies that have evolved in one phylum that distinguish it from other phyla. In order to 299
identify whether high taxonomic-level successional patterns could be identified in this study, we 300
utilised a high-density microarray approach, which allows identification of almost 104 taxa and 301
can detect variation in abundance over five orders of magnitude (7). The microarray approach is 302
potentially subject to PCR-bias as is the case in all end-point PCR approaches. However, 303
potential biases were minimised in this study by combining replicate PCRs, using the minimum 304
number of amplification cycles possible (56), using fast temperature ramping during cycling 305
(38), and using log-transformed abundance data (10). While the microarray approach is limited 306
to the identification of known taxa for which probes have been designed, previous comparisons 307
with clone library composition have confirmed the comprehensive coverage provided by the 308
PhyloChip (6, 15). 309
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Previously, we have shown that the microbial quotient (Cmic: Corg) is low in early 310
rehabilitation (4). This may favour r-strategist populations, indicative of an uncrowded 311
environment with higher resource availability (3). Conditions would shift in favour of K-312
strategists as the rehabilitation matures and the microbial quotient recovers. The existence of 313
such a trend was supported by the changes in relative abundance of Bacteroidetes and β-314
Proteobacteria, many of which (although not all) exhibit r-strategist attributes (19). Conversely, 315
the lack of change in Acidobacteria or Actinobacteria relative abundance, many of which exhibit 316
K-strategist attributes, did not fit this trend. However, knowledge of the ecological niches of 317
many bacterial phyla remains limited and classification as r- or K-strategists may not always be 318
relevant (19). Physiological attributes, other than growth metabolic strategies, may also 319
significantly influence a microorganism’s competitive ability. For example, the higher relative 320
abundance of Firmicutes in early rehabilitation is likely related to their ability to form spores 321
(e.g. Bacilli and Clostridia). Other phyla, such as Veruccomicrobia and Planctomycetes, have 322
few cultivated members and little is known of their growth or other ecological attributes (46, 54). 323
Further exploration of the response of phyla, or other high taxonomic groupings, to stress or 324
disturbance is needed to understand their ecological roles. Nevertheless, the microarray analysis 325
in this study revealed different successional patterns for individual bacterial phyla in 326
rehabilitation forest soils following bauxite mining and supported the trend found by community 327
structure profiling of an increased similarity to non-mined forest soils with rehabilitation age. 328
329
Acknowledgements 330
This research was supported by the Australian Research Council under the Linkage Program 331
scheme with industry partner Alcoa of Australia and a UWA Faculty of Natural and Agricultural 332
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Sciences start-up grant to support the collaboration with Lawrence Berkeley National 333
Laboratory. Support for D.B. Gleeson was provided by an Australian Research Council 334
Discovery Grant (DP0985832). Part of this work was supported in by the U.S. Department of 335
Energy under Contract No. DE-AC02-05CH11231 and by Laboratory Directed Research and 336
Development awards to E.L.Brodie. 337
338
References 339
1. Allison, V. J., Z. Yermakov, R. M. Miller, J. D. Jastrow, and R. Matamala. 2007. Using 340
landscape and depth gradients to decouple the impact of correlated environmental variables on 341
soil microbial community composition. Soil Biol. Biochem. 39:505-516. 342
2. Anderson, M. J. 2001. A new method for non-parametric multivariate analysis of variance. 343
Austral Ecol 26:32-46. 344
3. Andrews, J. H. (ed.). 1991. Comparative Ecology of Microorganisms and Macroorganisms. 345
Springer-Verlag, New York. 346
4. Banning, N. C., C. D. Grant, D. L. Jones, and D. V. Murphy. 2008. Recovery of soil organic 347
matter, organic matter turnover and nitrogen cycling in a post-mining forest rehabilitation 348
chronosequence. Soil Biol. Biochem. 40:2021-2031. 349
5. Bardgett, R. D., E. Kandeler, D. Tscherko, P. J. Hobbs, T. M. Bezemer, T. H. Jones, and L. 350
J. Thompson. 1999. Below-ground microbial community development in a high temperature 351
world. Oikos 85:193-203. 352
6. Brodie, E. L., T. Z. DeSantis, D. C. Joyner, S. M. Baek, J. T. Larsen, G. L. Andersen, T. C. 353
Hazen, P. M. Richardson, D. J. Herman, T. K. Tokunaga, J. M. Wan, and M. K. Firestone. 354
2006. Application of a high-density oligonucleotide microarray approach to study bacterial 355
population dynamics during uranium reduction and reoxidation. Appl. Environ. Microbiol. 356
72:6288-6298. 357
on Septem
ber 11, 2018 by guesthttp://aem
.asm.org/
Dow
nloaded from
17
7. Brodie, E. L., T. Z. DeSantis, J. P. M. Parker, I. X. Zubietta, Y. M. Piceno, and G. L. 358
Andersen. 2007. Urban aerosols harbor diverse and dynamic bacterial populations. Proc. Natl. 359
Acad. Sci. USA 104:299-304. 360
8. Brundrett, M. C., and L. K. Abbott. 1991. Roots of jarrah forest plants .1. Mycorrhizal 361
associations of shrubs and herbaceous plants. Aust. J. Bot. 39:445-457. 362
9. Burke, D. J., J. C. Lopez-Gutierrez, K. A. Smemo, and C. R. Chan. 2009. Vegetation and soil 363
environment influence the spatial distribution of root-associated fungi in a mature beech-maple 364
forest. Appl. Environ. Microbiol. 75:7639-7648. 365
10. Cao, Y., D. D. Williams, and N. E. Williams. 1999. Data transformation and standardization in 366
the multivariate analysis of river water quality. Ecol. Appl. 9:669-677. 367
11. Carney, K., and P. Matson. 2005. Plant communities, soil microorganisms, and soil carbon 368
cycling: does altering the world belowground matter to ecosystem functioning? Ecosystems 369
8:928-940. 370
12. Carson, J. K., D. Rooney, D. B. Gleeson, and N. Clipson. 2007. Altering the mineral 371
composition of soil causes a shift in microbial community structure. FEMS Micro. Ecol. 61:414-372
423. 373
13. Chambers, J. M. 2008. Software for Data Analysis: Programming with R. . Springer, New York, 374
USA. 375
14. Clarke, K. R., and R. N. Gorley. 2006. PRIMER v6: user manual/tutorial. PRIMER-E Ltd, 376
Plymouth, U.K. 377
15. Cruz-Martinez, K., K. B. Suttle, E. L. Brodie, M. E. Power, G. L. Andersen, and J. F. 378
Banfield. 2009. Despite strong seasonal responses, soil microbial consortia are more resilient to 379
long-term changes in rainfall than overlying grassland. ISME J. 3:738-744. 380
16. DeAngelis, K. M., E. L. Brodie, T. Z. DeSantis, G. L. Andersen, S. E. Lindow, and M. K. 381
Firestone. 2009. Selective progressive response of soil microbial community to wild oat roots. 382
ISME J. 3:168-178. 383
on Septem
ber 11, 2018 by guesthttp://aem
.asm.org/
Dow
nloaded from
18
17. Drenovsky, R. E., D. Vo, K. J. Graham, and K. M. Scow. 2004. Soil water content and organic 384
carbon availability are major determinants of soil microbial community composition. Microb. 385
Ecol. 48:424-430. 386
18. Felske, A., A. Wolterink, R. Van Lis, W. M. De Vos, and A. D. L. Akkermans. 2000. 387
Response of a soil bacterial community to grassland succession as monitored by 16S rRNA levels 388
of the predominant ribotypes. Appl. Environ. Microbiol. 66:3998-4003. 389
19. Fierer, N., M. A. Bradford, and R. B. Jackson. 2007. Toward an ecological classification of 390
soil bacteria. Ecology 88:1354-1364. 391
20. Fierer, N., and R. B. Jackson. 2006. The diversity and biogeography of soil bacterial 392
communities. Proc. Natl. Acad. Sci. USA 103:626-631. 393
21. Gardes, M., and T. D. Bruns. 1993. ITS primers with enhanced specificity for Basidiomycetes - 394
application to the identification of mycorrhizae and rusts. Mol. Ecol. 2:113-118. 395
22. Gleeson, D., N. Clipson, K. Melville, G. Gadd, and F. McDermott. 2005. Characterization of 396
fungal community structure on a weathered pegmatitic granite. Microb. Ecol. 50:360-368. 397
23. Gleeson, D., F. McDermott, and N. Clipson. 2006. Structural diversity of bacterial communities 398
in a heavy metal mineralized granite outcrop. Environ. Microbiol. 8:383-393. 399
24. Glen, M., N. L. Bougher, I. J. Colquhoun, S. Vlahos, W. A. Loneragan, P. A. O'Brien, and 400
G. E. S. J. Hardy. 2008. Ectomycorrhizal fungal communities of rehabilitated bauxite mines and 401
adjacent, natural jarrah forest in Western Australia. Forest Ecology and Management 255:214-402
225. 403
25. Grant, C. D. 2006. State-and-transition successional model for bauxite mining rehabilitation in 404
the jarrah forest of Western Australia. Restor. Ecol. 14:28-37. 405
26. Grierson, P. F., and M. A. Adams. 2000. Plant species affect acid phosphatase, ergosterol and 406
microbial P in a Jarrah (Eucalyptus marginata Donn ex Sm.) forest in south-western Australia. 407
Soil Biol. Biochem. 32:1817-1827. 408
on Septem
ber 11, 2018 by guesthttp://aem
.asm.org/
Dow
nloaded from
19
27. Gros, R., L. Jocteur Monrozier, and P. Faivre. 2006. Does disturbance and restoration of 409
alpine grassland soils affect the genetic structure and diversity of bacterial and N2-fixing 410
populations? Environ. Microbiol. 8:1889-1901. 411
28. Hackl, E., S. Zechmeister-Boltenstern, L. Bodrossy, and A. Sessitsch. 2004. Comparison of 412
diversities and compositions of bacterial populations inhabiting natural forest soils. Appl. 413
Environ. Microbiol. 70:5057-5065. 414
29. Harris, J. 2009. Soil microbial communities and restoration ecology: facilitators or followers? 415
Science 325:573-574. 416
30. Harris, J. A. 2003. Measurements of the soil microbial community for estimating the success of 417
restoration. Eur. J. Soil Sci. 54:801-808. 418
31. Havel, J. 1975. Site-vegetation mapping in the norther jarrah forest (Darling Range). 1. 419
Definition of site-vegetation types. Bulletin 86. Forest Department Western Australia. 420
32. Hedlund, K., B. Griffiths, S. Christensen, S. Scheu, H. Setälä, T. Tscharntke, and H. 421
Verhoef. 2004. Trophic interactions in changing landscapes: responses of soil food webs. Basic 422
Appl. Ecol. 5:495-503. 423
33. Jangid, K., M. A. Williams, A. J. Franzluebbers, J. M. Blair, D. C. Coleman, and W. B. 424
Whitman. 2010. Development of soil microbial communities during tallgrass prairie restoration. 425
Soil Biol. Biochem. 42:302-312. 426
34. Janssen, P. H. 2006. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 427
16S rRNA genes. Appl. Environ. Microbiol. 72:1719-1728. 428
35. Kasel, S., L. T. Bennett, and J. Tibbits. 2008. Land use influences soil fungal community 429
composition across central Victoria, south-eastern Australia. Soil Biol. Biochem. 40:1724-1732. 430
36. Koch, J. M. 2007. Alcoa's mining and restoration process in South Western Australia. Restor. 431
Ecol. 15:S11-S16. 432
on Septem
ber 11, 2018 by guesthttp://aem
.asm.org/
Dow
nloaded from
20
37. Kuramae, E. E., H. A. Gamper, E. Yergeau, Y. M. Piceno, E. L. Brodie, T. Z. DeSantis, G. 433
L. Andersen, J. A. van Veen, and G. A. Kowalchuk. 2010. Microbial secondary succession in a 434
chronosequence of chalk grasslands. ISME J. 4:711-715. 435
38. Kurata, S., T. Kanagawa, Y. Magariyama, K. Takatsu, K. Yamada, T. Yokomaku, and Y. 436
Kamagata. 2004. Reevaluation and reduction of a PCR bias caused by reannealing of templates. 437
Appl. Environ. Microbiol. 70:7545-7549. 438
39. Lalor, B. M., W. R. Cookson, and D. V. Murphy. 2007. Comparison of two methods that 439
assess soil community level physiological profiles in a forest ecosystem. Soil Biol. Biochem. 440
39:454-462. 441
40. Lauber, C. L., M. S. Strickland, M. A. Bradford, and N. Fierer. 2008. The influence of soil 442
properties on the structure of bacterial and fungal communities across land-use types. Soil Biol. 443
Biochem. 40:2407-2415. 444
41. Lewis, D. E., J. R. White, D. Wafula, R. Athar, T. Dickerson, H. N. Williams, and A. 445
Chauhan. 2010. Soil functional diversity analysis of a bauxite-mined restoration 446
chronosequence. Microb. Ecol. 59:710-723. 447
42. McArthur, W. M. 2004. Reference soils of south-western Australia. Department of Agriculture, 448
Perth, Western Australia. 449
43. McKinley, V. L., A. D. Peacock, and D. C. White. 2005. Microbial community PLFA and PHB 450
responses to ecosystem restoration in tallgrass prairie soils. Soil Biol. Biochem. 37:1946-1958. 451
44. Norman, M. A., J. M. Koch, C. D. Grant, T. K. Morald, and S. C. Ward. 2006. Vegetation 452
succession after bauxite mining in Western Australia. Restor. Ecol. 14:278-288. 453
45. Normand, P., C. Ponsonnet, X. Nesme, M. Neyra, and P. Simonet. 1996. ITS analysis of 454
prokaryotes, p. 1-12. In A. D. Akkermans, J. D. van Elsas, and E. I. deBruijn (ed.), Molecular 455
Microbial Ecology Manual. Kluwer Academic Press, Amsterdam. 456
46. Nunes da Rocha, U., L. Van Overbeek, and J. D. Van Elsas. 2009. Exploration of hitherto-457
uncultured bacteria from the rhizosphere. FEMS Micro. Ecol. 69:313-328. 458
on Septem
ber 11, 2018 by guesthttp://aem
.asm.org/
Dow
nloaded from
21
47. Pennanen, T., S. Caul, T. J. Daniell, B. S. Griffiths, K. Ritz, and R. E. Wheatley. 2004. 459
Community-level responses of metabolically-active soil microorganisms to the quantity and 460
quality of substrate inputs. Soil Biol. Biochem. 36:841-848. 461
48. Pennanen, T., J. Liski, E. Bååth, V. Kitunen, J. Uotila, C. J. Westman, and H. Fritze. 1999. 462
Structure of the microbial communities in coniferous forest soils in relation to site fertility and 463
stand development stage. Microb. Ecol. 38:168-179. 464
49. Philippot, L., S. G. E. Andersson, T. J. Battin, J. I. Prosser, J. P. Schimel, W. B. Whitman, 465
and S. Hallin. 2010. The ecological coherence of high bacterial taxonomic ranks. Nat. Rev. 466
Micro. 8:523-529. 467
50. Philippot, L., D. Bru, N. P. A. Saby, J. Cuhel, D. Arrouays, M. Simek, and S. Hallin. 2009. 468
Spatial patterns of bacterial taxa in nature reflect ecological traits of deep branches of the 16S 469
rRNA bacterial tree. Environ. Microbiol. 11:3096-3104. 470
51. Ranjard, L., F. Poly, J.-C. Lata, C. Mougel, J. Thioulouse, and S. Nazaret. 2001. 471
Characterization of bacterial and fungal soil communities by automated ribosomal intergenic 472
spacer analysis fingerprints: biological and methodological variability. Appl. Environ. Microbiol. 473
67:4479-4487. 474
52. Rousk, J., E. Baath, P. C. Brookes, C. L. Lauber, C. Lozupone, J. G. Caporaso, R. Knight, 475
and N. Fierer. 2010. Soil bacterial and fungal communities across a pH gradient in an arable soil. 476
ISME J. 4:1340-1351. 477
53. Ruppel, S., V. Torsvik, F. L. Daae, L. Ovreas, and J. Ruhlmann. 2007. Nitrogen availability 478
decreases prokaryotic diversity in sandy soils. Biol. Fert. Soils 43:449-459. 479
54. Sangwan, P., S. Kovac, K. E. R. Davis, M. Sait, and P. H. Janssen. 2005. Detection and 480
cultivation of soil Verrucomicrobia. Appl. Environ. Microbiol. 71:8402-8410. 481
55. Scallan, U., A. Liliensiek, N. Clipson, and J. Connolly. 2008. Ribosort: a program for 482
automated data preparation and exploratory analysis of microbial community fingerprints. Mol. 483
Ecol. Res. 8:95-98. 484
on Septem
ber 11, 2018 by guesthttp://aem
.asm.org/
Dow
nloaded from
22
56. Suzuki, M., and S. Giovannoni. 1996. Bias caused by template annealing in the amplification of 485
mixtures of 16S rRNA genes by PCR. Appl. Environ. Microbiol. 62:625-630. 486
57. Tilman, D. 1985. The resource-ratio hypothesis of plant succession. Am. Nat. 125:827-852. 487
58. Todd, M. C. L., P. F. Grierson, and M. A. Adams. 2000. Litter cover as an index of nitrogen 488
availability in rehabilitated mine sites. Aust. J. Soil Res. 38:423-434. 489
59. Torsvik, V., and L. Ovreas. 2002. Microbial diversity and function in soil: from genes to 490
ecosystems. Curr. Opin. Microbiol. 5:240-245. 491
60. van Dijk, J., W. A. M. Didden, F. Kuenen, P. M. van Bodegom, H. A. Verhoef, and R. Aerts. 492
2009. Can differences in soil community composition after peat meadow restoration lead to 493
different decomposition and mineralization rates? Soil Biol. Biochem. 41:1717-1725. 494
61. Wakelin, S. A., L. M. Macdonald, S. L. Rogers, A. L. Gregg, T. P. Bolger, and J. A. 495
Baldock. 2008. Habitat selective factors influencing the structural composition and functional 496
capacity of microbial communities in agricultural soils. Soil Biol. Biochem. 40:803-813. 497
62. White, T. J., T. Bruns, S. Lee, and J. Taylor. 1990. Amplification and direct sequencing of 498
fungal ribosomal RNA genes for phylogenetics, p. 315-322. In M. A. Innis, D. H. Gelfand, J. J. 499
Sninsky, and T. J. White (ed.), PCR Protocols: A Guide to Methods and Applications. Academic 500
Press, New York. 501
63. Wilson, K. H., R. B. Blitchington, and R. C. Greene. 1990. Amplification of bacterial 16S 502
ribosomal DNA with polymerase chain reaction. J. Clin. Microbiol. 28:1942-1946. 503
64. Youssef, N. H., and M. S. Elshahed. 2008. Diversity rankings among bacterial lineages in soil. 504
ISME J. 3:305-313. 505
506
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Figure 1. Principle coordinate analysis (PCO) of (a) bacterial community structure by ARISA, 508
(b) fungal community structure by ARISA of a jarrah forest rehabilitation chronosequence and 509
non-mined reference soils and (c) bacterial community structure by microarray of a sample sub-510
set (rehabilitation soils from furrows and non-mined S-type forest only). Labels for (a) and (b) 511
are M: mound soils; F: furrow soils; NM: non-mined soils of S and TS forest site-vegetation 512
types. Vectors show Pearson correlations with six selected soil characteristics. Abbreviations are 513
as follows: Ctot: total C; Ntot: total N; Cmic: microbial biomass C; P: available (colwell) P. 514
515
Figure 2. Changes in mean relative abundance of individual soil bacterial phyla exhibiting a 516
decreasing trend (a), increasing trend (b) or no change or trend (c) with rehabilitation age; and of 517
classes of Proteobacteria (d) in a jarrah forest rehabilitation chronosequence, as determined by 518
microarray analysis of 16S rRNA genes using the PhyloChip. Data has been normalised to the 519
mean relative abundance of each phyla or class found within non-mined reference soil 520
represented by the dotted line at y=1. 521
522
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Table 1. Distance-based multivariate multiple regression (DistlmF) showing relationships between soil
characteristics and bacterial and fungal community structures by ARISA and bacterial community
structure by microarray. Results from a forward selection model are shown, with only variables (Var.)
that contributed significantly to the model. Significance of the relationships (P) and the cumulative
percentage of variance explained (Prop.) is shown. Abbreviations are as follows; EC: electrical
conductivity; Ctot: total C; Ntot: total N; Csol org: soluble organic C, Cmic: microbial biomass C; Cmic:Corg:
microbial quotient; Ninorg: inorganic N (ammonium + nitrate); P: available (Colwell) P.
Bacterial ARISA Fungal ARISA Bacterial microarray
Var. P Prop. Var. P Prop. Var. P Prop.
Cmic:Corg *** 10.9 pH *** 11.0 pH *** 25.3
Ctot *** 15.3 Cmic *** 15.6 EC *** 39.2
Ntot *** 20.0 C:N tot *** 19.0 P * 45.9
P *** 23.7 Cmic:Corg *** 21.9 Cmic * 52.5
Cmic *** 27.1 Ctot *** 25.1 Csol org * 58.5
pH *** 29.8 Ntot *** 27.9 Ctot * 65.2
Csol org * 31.9
Ninorg * 33.9 %Clay+Silt * 36.0
* P< 0.1, ** P < 0.05, ***P < 0.005
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-60 -40 -20 0 20 40
PCO1 (14.6% of total variation)
-40
-20
0
20
40
PC
O2
(8. 5
%oft o
t al v
ariati o
n)
Age061418NM
MM
M
M
M
F
F
F F
F
M
M
M
M
M
FF
F
FF
MM
MM
M FF
F
FF
S
S
S
S
S
TS
T
TSTS
TS
M M
M
M
MF
F
F
FF
pH
CtotNtot
C:Ntot
Cmic
P
-60 -40 -20 0 20 40
PCO1 (13.6% of total variation)
-40
-20
0
20
40
PC
O2
(9.6
%ofto
talv
aria
t ion)
MM
MM
MF F F
F
F
M
M
M
M
MFF
F
FF
M
M
M
M
M
FF
F
F
F
SS S
S
S
TS
TSTSTS
TS
MM
MM
M
F F
F
F
F
pH
CtotNtot
C:Ntot
Cmic
P
(a)
(b)
-10 -5 0 5 10PCO1 (31.4% of total variation)
-10
-5
0
5
10
F
F
F
F
F
F
F
F
F
FF
F
SS
S
pH Ctot
NtotC:Ntot
Cmic
P
(c)
PC
O2
(17
.2%
of
tota
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)
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Re
ha
bilita
tion
ag
e (y
)
Mean relative abundance of individual phyla or class; normalised to non-mined forest mean abundance
(a)
(b)
(c)
(d)
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